Data Analytics

The term Data Analytics refers to the process of analysing raw data in order to make conclusions about that information.

It can involve a lot of different techniques and steps involved including data mining, data management, statistical analysis and data presentation.

Types of Data Analytics

There are broadly four types of data analytics:

1. Descriptive Analytics

Descriptive analytics is used to help answer questions about what happened. These techniques summarise large datasets to describe outcomes to stakeholders.

By developing key performance indicators (KPIs,) these strategies can help track successes or failures using metrics such as return on investment (ROI) or call centre specific metric like Average Speed of Answer (ASA), Grade of Service (GOS) and so on.

This process requires the collection of relevant data, processing of the data, data analysis and data visualisation through dashboards etc.

This process provides essential insight into past performance e.g. what did we achieve yesterday, last week, last month etc.

2. Diagnostic Analytics

Diagnostic analytics is used to help answer questions about why things happened.  They take the findings from descriptive analytics to investigate further in identifying the cause of the performance.

This generally occurs in three steps:

    • Identify anomalies/outliers in the data. These may be unexpected changes in a metric.
    • Data that is related to these anomalies is collected.
    • Statistical techniques are used to find relationships and trends that explain these anomalies (e.g. when it rains call volumes always increase by 25%).



3. Predictive Analytics

Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur and is widely applied to Workforce Optimisation and Forecasting (e.g. Every Monday following a full moon call volumes will increase by 10%).

Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.

4. Prescriptive Analytics

Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven/informed decisions can be made.

Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets e.g. Big Data. By analysing past decisions and events, the likelihood of different outcomes can be estimated.

Benefits of Data Analytics

Ultimately the goal of data analytics is to help businesses and organisations to succeed. This can be through a variety of means including:

  • Improved efficiency
  • Better customer experiences
  • Increased profitability
  • Improved employee satisfaction and retention

Related terms include:

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